The traditional functional link artificial neural network (FLANN) is inefficient in the high-dimensional data modeling of the chemical process

where the data has characteristics of multi-dimensional

strongly coupled and noisy. In order to dealing with this problem

the FLANN based on analytical hierarchy process (AHP-FLANN) is proposed. The analytical hierarchy process (AHP) is constructed to filter redundant information and extract characteristic components. And then these characteristic components are trained by the FLANN. Meanwhile

the proposed AHP-FLANN method is applied to analyze the ethylene production data in the chemical industry. Compared with the BP network and the FLANN

the AHP-FLANN has the advantages of fast convergence speed with high modeling accuracy and strong network stability. The experimental result shows that the proposed method can guide the ethylene production conditions and improve the efficiency of energy utilization during ethylene production process. It has the practical value in practice.